An important area in digital image analysis in the healthcare field is the ability to identify and quantify staining for analytes of interest in specific subcellular locations. Algorithms for doing such analyses currently exist. For example, algorithms are currently known for detecting membrane expression of HER2 and cMET. These algorithms rely on nuclei detection to search for stained membrane on a local neighborhood around nuclei, using a predefined threshold to define the neighborhood around the nuclei to be searched for membrane regions. Hence, if these algorithms miss the nuclei or if the membrane lies outside the predefined neighborhood radius, stained membrane around them will not be detected. Additionally, the algorithms ignore regions that contain membrane staining in combination with other staining compartments (such as cytoplasmic staining). Thus, quantification of staining using these methods could be incomplete or incorrect.
An important area in digital image analysis in the healthcare field is the ability to identify and quantify staining for analytes of interest in specific subcellular locations. Algorithms for doing such analyses currently exist. For example, algorithms are currently known for detecting membrane expression of HER2 and cMET. These algorithms rely on nuclei detection to search for stained membrane on a local neighborhood around nuclei, using a predefined threshold to define the neighborhood around the nuclei to be searched for membrane regions.
However, if these algorithms miss the nuclei or if the membrane lies outside the predefined neighborhood radius, stained membrane around them will not be detected. Additionally, the algorithms ignore regions that contain membrane staining in combination with other staining compartments (such as cytoplasmic staining). Thus, quantification of staining using these methods could be incomplete or incorrect. We are not aware of any existing solutions for these deficiencies.